03-embeddings-search.md

layout	default
title	Chapter 3: Embeddings and Search
nav_order	3
parent	OpenAI Python SDK Tutorial

Chapter 3: Embeddings and Search

Welcome to Chapter 3: Embeddings and Search. In this part of OpenAI Python SDK Tutorial: Production API Patterns, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

Embeddings power retrieval quality in most production RAG systems.

Create Embeddings

from openai import OpenAI

client = OpenAI()

docs = [
    "Retry transient failures with exponential backoff.",
    "Use idempotency keys for side-effecting writes.",
    "Track p95 and p99 latency separately."
]

emb = client.embeddings.create(
    model="text-embedding-3-small",
    input=docs,
)

vectors = [row.embedding for row in emb.data]
print(len(vectors), len(vectors[0]))

Retrieval Pipeline Blueprint

1. chunk source docs with semantic boundaries
2. generate embeddings and store vectors + metadata
3. retrieve top-k candidates by similarity
4. re-rank or filter by business constraints
5. pass compact context to generation layer

Quality Controls

• maintain versioned chunking strategy
• keep source timestamps for freshness checks
• evaluate retrieval quality with labeled benchmark queries

Summary

You now have the core pieces to build and evaluate a robust embeddings-backed retrieval system.

Next: Chapter 4: Agents and Assistants

Source Code Walkthrough

examples/parsing_tools.py

The Column class in examples/parsing_tools.py handles a key part of this chapter's functionality:

class Column(str, Enum):
    id = "id"
    status = "status"
    expected_delivery_date = "expected_delivery_date"
    delivered_at = "delivered_at"
    shipped_at = "shipped_at"
    ordered_at = "ordered_at"
    canceled_at = "canceled_at"


class Operator(str, Enum):
    eq = "="
    gt = ">"
    lt = "<"
    le = "<="
    ge = ">="
    ne = "!="


class OrderBy(str, Enum):
    asc = "asc"
    desc = "desc"


class DynamicValue(BaseModel):
    column_name: str


class Condition(BaseModel):
    column: str

This class is important because it defines how OpenAI Python SDK Tutorial: Production API Patterns implements the patterns covered in this chapter.

examples/parsing_tools.py

The Operator class in examples/parsing_tools.py handles a key part of this chapter's functionality:

class Operator(str, Enum):
    eq = "="
    gt = ">"
    lt = "<"
    le = "<="
    ge = ">="
    ne = "!="


class OrderBy(str, Enum):
    asc = "asc"
    desc = "desc"


class DynamicValue(BaseModel):
    column_name: str


class Condition(BaseModel):
    column: str
    operator: Operator
    value: Union[str, int, DynamicValue]


class Query(BaseModel):
    table_name: Table
    columns: List[Column]
    conditions: List[Condition]
    order_by: OrderBy

This class is important because it defines how OpenAI Python SDK Tutorial: Production API Patterns implements the patterns covered in this chapter.

examples/parsing_tools.py

The OrderBy class in examples/parsing_tools.py handles a key part of this chapter's functionality:

class OrderBy(str, Enum):
    asc = "asc"
    desc = "desc"


class DynamicValue(BaseModel):
    column_name: str


class Condition(BaseModel):
    column: str
    operator: Operator
    value: Union[str, int, DynamicValue]


class Query(BaseModel):
    table_name: Table
    columns: List[Column]
    conditions: List[Condition]
    order_by: OrderBy


client = OpenAI()

completion = client.chat.completions.parse(
    model="gpt-4o-2024-08-06",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant. The current date is August 6, 2024. You help users query for the data they are looking for by calling the query function.",

This class is important because it defines how OpenAI Python SDK Tutorial: Production API Patterns implements the patterns covered in this chapter.

How These Components Connect

flowchart TD
    A[Column]
    B[Operator]
    C[OrderBy]
    A --> B
    B --> C

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